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A novel method based on deep learning for aligned fingerprints matching

  • Yonghong Liu
  • Baicun Zhou
  • Congying HanEmail author
  • Tiande Guo
  • Jin Qin
Article
  • 101 Downloads

Abstract

In this study, a novel method based on deep learning for aligned fingerprints matching is proposed. According to the characteristics of fingerprint images, a convolutional network, Finger ConvNet, is designed. In addition, a new joint supervision signal is used to train Finger ConvNet to obtain deep features. Experimental studies are performed on public fingerprint datasets, the ID Card fingerprint dataset and the Ten-Finger Fingerprint Card fingerprint dataset. Furthermore, four performance indicators, the false matching rate (FMR), false non-matching rate (FNMR), equal error rate (EER) and receiver operating characteristic (ROC) curve, are measured. The experimental results demonstrate the effectiveness of the proposed method, which achieved a competitive effect in comparison with conventional fingerprint matching algorithms in fingerprint verification tasks using the FVC2000, FVC2002, and FVC2004 datasets. Moreover, the matching speed of the proposed method was almost 5 times faster than the fastest conventional fingerprint matching algorithms. In addition, it can be used as a fast matching method to filter out many templates with low scores by setting a threshold according to the matching scores and thus accelerate the process in identification tasks.

Keywords

Fingerprint matching Deep learning Finger ConvNet Fast matching method 

Notes

Acknowledgment

This research was funded by the State Key Program of National Natural Science Foundation of China under grant number 11731013 and 11331012, and by the National Natural Science Foundation of China under grant number 11571014.

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  • Yonghong Liu
    • 1
  • Baicun Zhou
    • 1
  • Congying Han
    • 1
    • 2
    Email author
  • Tiande Guo
    • 1
    • 2
  • Jin Qin
    • 1
  1. 1.University of Chinese Academy of Sciences (UCAS)BeijingChina
  2. 2.Key Laboratory of Big Data Mining and Knowledge Management, CASBeijingChina

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